list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent. The description adds value by detailing the returned fields and noting that only active subscriptions are returned by default, which is beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-load the purpose and provide additional context with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional boolean parameter and no output schema, the description adequately covers purpose, returned fields, and usage guidance. It is self-contained and complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter (include_inactive) is fully described in the schema. The description implies its effect by saying 'active subscriptions', but adds no additional semantics beyond the schema. Baseline 3 due to high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'List the caller's active subscriptions' – a specific verb and resource. It also lists returned fields (id, type, params, etc.), distinguishing it from sibling tools like subscribe and unsubscribe.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit use cases: 'review what you're monitoring before adding more or to find an id to cancel.' This provides context for when to use, though it doesn't explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set includes three nearly-identical ask_pipeworx variants (base, beta, grounded) that differ only subtly, and deep_research overlaps with ask_pipeworx for multi-part queries. Several company/prediction tools also share adjacent purposes (entity_profile vs recent_changes vs compare_entities; polymarket_arbitrage vs polymarket_edges), though detailed descriptions help. Overall, an agent could mis-select between these overlapping tools.
Most tools follow a verb_noun or consistent prefix pattern (opendosm_*, polymarket_*), and the ask_pipeworx family is internally consistent. However, a few are noun phrases (entity_profile, ai_visibility_check, recent_alerts) and some verbs aren't uniform (list_datasets vs dataset_meta vs get_dataset). The mixture is readable but not fully consistent.
34 tools is well above the typical focused server range and includes a clearly redundant experimental variant (ask_pipeworx_beta) plus many loosely-related utility functions (memory, subscriptions, dependency scanning, llms.txt generation). While the broad scope justifies some size, the count feels excessive for the 'Opendosm My' name, which suggests a narrower Malaysian-statistics focus.
For the apparent overarching goal of a multi-domain research/query platform, the surface is quite complete: data lookup, deep research, entity comparison, claim verification, prediction-market analysis, memory, and subscriptions are all covered. The Malaysian OpenDOSM component itself has list/meta/get lifecycle. Minor gaps (e.g., no direct dataset search beyond curated lists, no way to execute arbitrary Pipeworx tools directly) are workable.